Hands-on Exercise 3

Part 1: Programming Interactive Data Visualisation with R

1.1 Getting Started

To begin this exercise, load the necessary R packages that support interactive and linked visualisations.

pacman::p_load(ggiraph, plotly, 
               patchwork, DT, tidyverse) 

1.2 Importing Data

We’ll work with a dataset containing students’ exam scores. This data will be used throughout the exercise to create various visualisations.

exam_data <- read_csv("Data/Exam_data.csv")

Rows: 322 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): ID, CLASS, GENDER, RACE
dbl (3): ENGLISH, MATHS, SCIENCE

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

1.3 Interactive Visualisation with ggiraph

1.3.1 Dot Plot with Tooltips

This plot introduces basic interactivity where hovering over dots reveals student IDs.

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(
    aes(tooltip = ID),
    stackgroups = TRUE, 
    binwidth = 1, 
    method = "histodot") +
  scale_y_continuous(NULL, 
                     breaks = NULL)
girafe(
  ggobj = p,
  width_svg = 6,
  height_svg = 6*0.618
)

1.3.2 Displaying Multiple Tooltip Fields

Add richer information to tooltips by concatenating ID and class.

exam_data$tooltip <- c(paste0(     
  "Name = ", exam_data$ID,         
  "\n Class = ", exam_data$CLASS)) 

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(
    aes(tooltip = exam_data$tooltip), 
    stackgroups = TRUE,
    binwidth = 1,
    method = "histodot") +
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(
  ggobj = p,
  width_svg = 8,
  height_svg = 8*0.618
)

1.3.3 Customising Tooltip style

Modify the tooltip appearance using inline CSS.

tooltip_css <- "background-color:white; #<<
font-style:bold; color:black;" #<<

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(tooltip = ID),                   
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618,
  options = list(    #<<
    opts_tooltip(    #<<
      css = tooltip_css)) #<<
)                                        

1.3.4 Displaying Summary Statistics in Tooltip

Show aggregate values like mean and SEM with hover feedback.

tooltip <- function(y, ymax, accuracy = .01) {
  mean <- scales::number(y, accuracy = accuracy)
  sem <- scales::number(ymax - y, accuracy = accuracy)
  paste("Mean maths scores:", mean, "+/-", sem)
}

gg_point <- ggplot(data=exam_data, 
                   aes(x = RACE),
) +
  stat_summary(aes(y = MATHS, 
                   tooltip = after_stat(  
                     tooltip(y, ymax))),  
    fun.data = "mean_se", 
    geom = GeomInteractiveCol,  
    fill = "light blue"
  ) +
  stat_summary(aes(y = MATHS),
    fun.data = mean_se,
    geom = "errorbar", width = 0.2, size = 0.2
  )

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

girafe(ggobj = gg_point,
       width_svg = 8,
       height_svg = 8*0.618)

Things to learn from the code chunk above

• replace the size argument with linewidth ingeom = "errorbar"layer to resolve the warning.

Here is the corrected version of the code:

tooltip <- function(y, ymax, accuracy = .01) {
  mean <- scales::number(y, accuracy = accuracy)
  sem <- scales::number(ymax - y, accuracy = accuracy)
  paste("Mean maths scores:", mean, "+/-", sem)
}

gg_point <- ggplot(data=exam_data, 
                   aes(x = RACE),
) +
  stat_summary(aes(y = MATHS, 
                   tooltip = after_stat(  
                     tooltip(y, ymax))),  
    fun.data = "mean_se", 
    geom = GeomInteractiveCol,  
    fill = "light blue"
  ) +
  stat_summary(aes(y = MATHS),
    fun.data = mean_se,
    geom = "errorbar", width = 0.2, linewidth = 0.2
)

girafe(ggobj = gg_point,
       width_svg = 8,
       height_svg = 8*0.618)

1.3.5 Hover Effect with data_id

Implement visual effects such as highlighting and opacity change on hover.

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(           
    aes(data_id = CLASS),             
    stackgroups = TRUE,               
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618                      
)                                        

1.3.6 Styling the Hover Effect

Implement visual effects such as highlighting and opacity change on hover.

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(data_id = CLASS),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618,
  options = list(                        
    opts_hover(css = "fill: #202020;"),  
    opts_hover_inv(css = "opacity:0.2;") 
  )                                        
)                                        

1.3.7 Tooltip + Hover Integration

Implement visual effects such as highlighting and opacity change on hover.

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(tooltip = CLASS, 
        data_id = CLASS),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618,
  options = list(                        
    opts_hover(css = "fill: #202020;"),  
    opts_hover_inv(css = "opacity:0.2;") 
  )                                        
)                                        

1.3.8 Adding Click Actions with onclick

Clicking a dot opens a URL with more student information.

exam_data$onclick <- sprintf("window.open(\"%s%s\")",
"https://www.moe.gov.sg/schoolfinder?journey=Primary%20school",
as.character(exam_data$ID))

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(onclick = onclick),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618)                                        

1.3.9 Coordinated Views

Synchronise hover effects across multiple views using shared IDs.

p1 <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(data_id = ID),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +  
  coord_cartesian(xlim=c(0,100)) + 
  scale_y_continuous(NULL,               
                     breaks = NULL)

p2 <- ggplot(data=exam_data, 
       aes(x = ENGLISH)) +
  geom_dotplot_interactive(              
    aes(data_id = ID),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") + 
  coord_cartesian(xlim=c(0,100)) + 
  scale_y_continuous(NULL,               
                     breaks = NULL)

girafe(code = print(p1 + p2), 
       width_svg = 6,
       height_svg = 3,
       options = list(
         opts_hover(css = "fill: #202020;"),
         opts_hover_inv(css = "opacity:0.2;")
         )
       ) 

1.4 Interactive Visualisation with Plotly

1.4.1 Scatter Plot

plot_ly(data = exam_data, 
             x = ~MATHS, 
             y = ~ENGLISH)

No trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plotly.com/r/reference/#scatter

No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

1.4.2 Encoding by Race

plot_ly(data = exam_data, 
        x = ~ENGLISH, 
        y = ~MATHS, 
        color = ~RACE)

No trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plotly.com/r/reference/#scatter

No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

1.4.3 ggplot Conversion

Convert existing ggplot2 objects into interactive plots.

p <- ggplot(data=exam_data, 
            aes(x = MATHS,
                y = ENGLISH)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))
ggplotly(p)

1.4.4 Coordinated Linked Views

Highlight observations across multiple charts using highlight_key() and subplot().

d <- highlight_key(exam_data)
p1 <- ggplot(data=d, 
            aes(x = MATHS,
                y = ENGLISH)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))

p2 <- ggplot(data=d, 
            aes(x = MATHS,
                y = SCIENCE)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))
subplot(ggplotly(p1),
        ggplotly(p2))

1.5 Crosstalk and DT

1.5.1 Interactive Table

Render a searchable, sortable, compact datatable using DT.

DT::datatable(exam_data, class= "compact")

1.5.2 Linked Table and Plot

Link DT tables and plots interactively using the crosstalk framework.

d <- highlight_key(exam_data) 
p <- ggplot(d, 
            aes(ENGLISH, 
                MATHS)) + 
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))

gg <- highlight(ggplotly(p),        
                "plotly_selected")  

crosstalk::bscols(gg,               
                  DT::datatable(d), 
                  widths = 5)        

Setting the `off` event (i.e., 'plotly_deselect') to match the `on` event (i.e., 'plotly_selected'). You can change this default via the `highlight()` function.

Part 2: Programming Animated Statistical Graphics with R

2.1 Getting Started

Loading the R packages

pacman::p_load(readxl, gifski, gapminder,
               plotly, gganimate, tidyverse)

2.2 Importing the data

Load population data from Excel and prepare it for time-series animation.

col <- c("Country", "Continent")
globalPop <- read_xls("Data/GlobalPopulation.xls",
                      sheet="Data") %>%
  mutate(across(col, as.factor)) %>%
  mutate(Year = as.integer(Year))

Warning: There was 1 warning in `mutate()`.
ℹ In argument: `across(col, as.factor)`.
Caused by warning:
! Using an external vector in selections was deprecated in tidyselect 1.1.0.
ℹ Please use `all_of()` or `any_of()` instead.
  # Was:
  data %>% select(col)

  # Now:
  data %>% select(all_of(col))

See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.

2.3 Animated Visualisation with gganimate

2.3.1 Building a static population bubble plot

Create a base plot using geom_point() with aesthetic mappings.

ggplot(globalPop, aes(x = Old, y = Young, 
                      size = Population, 
                      colour = Country)) +
  geom_point(alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', 
       x = '% Aged', 
       y = '% Young') 

2.3.2 Animated Plot

Use transition_time() and ease_aes() to animate transitions over time.

ggplot(globalPop, aes(x = Old, y = Young, 
                      size = Population, 
                      colour = Country)) +
  geom_point(alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', 
       x = '% Aged', 
       y = '% Young') +
  transition_time(Year) +       
  ease_aes('linear')          

2.4 Animated Visualisation with plotly

2.4.1 Using ggplotly()

Convert animated ggplot objects to interactive plotly charts.

gg <- ggplot(globalPop, 
       aes(x = Old, 
           y = Young, 
           size = Population, 
           colour = Country)) +
  geom_point(aes(size = Population,
                 frame = Year),
             alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', 
       y = '% Young')

Warning in geom_point(aes(size = Population, frame = Year), alpha = 0.7, :
Ignoring unknown aesthetics: frame

ggplotly(gg)

Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to
replace is not a multiple of replacement length

2.4.2 Fix Display Issues

Fix layout issues and hide unnecessary legends to clean up the output.

gg <- ggplot(globalPop, 
       aes(x = Old, 
           y = Young, 
           size = Population, 
           colour = Country)) +
  geom_point(aes(size = Population,
                 frame = Year),
             alpha = 0.7) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', 
       y = '% Young') + 
  theme(legend.position='none')

Warning in geom_point(aes(size = Population, frame = Year), alpha = 0.7):
Ignoring unknown aesthetics: frame

ggplotly(gg)

Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to
replace is not a multiple of replacement length

2.4.3 Directly with plot_ly()

Use plot_ly() directly to generate a bubble chart animated by year.

bp <- globalPop %>%
  plot_ly(x = ~Old, 
          y = ~Young, 
          size = ~Population, 
          color = ~Continent,
          sizes = c(2, 100),
          frame = ~Year, 
          text = ~Country, 
          hoverinfo = "text",
          type = 'scatter',
          mode = 'markers'
          ) %>%
  layout(showlegend = FALSE)
bp

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Summary

In this hands-on exercise, we learned how to create both interactive and animated data visualisations in R. Part 1 focused on interactive graphics using ggiraph, plotly, and DT for better data exploration and presentation. Part 2 introduced animated plots with gganimate and plotly to enhance time-based storytelling. These techniques are especially useful in building dashboards, reports, and applications where user engagement and clarity are key.